What Lessons can we Learn Netflix’s use of Big Data?

It is no secret Netflix’s success is built upon its innovative use of big data. By taking advantage of metrics which were previously unreachable by much of the industry, Netflix managed to not only offer fierce competition, but for some analysts, start the beginning of the end for cable television. So how was big data so influential in its success and what can other organisations learn from their model to disrupt their industries?

Avoid taking data purely at face value

Traditionally television channels relied on ‘test’ audiences, whereby groups of people were brought into a room and shown the pilot of a TV show. These individuals were selected as an attempt to offer as much of a cross section of society as possible.

However, test audiences offer only a sliver of data when it comes to what the population as a whole is interested in. For instance test audiences hated Seinfeld, yet loved another comedy called Sister Kate leading to NBC originally dismissing what many would say is the greatest comedy show of all time (bonus points if you actually remember Sister Kate!).

On the other hand, Netflix does not use test audiences. Instead Netflix turns to its big data algorithms to more accurately capture a snapshot of its viewership. When deciding whether to give House of Cards the greenlight, Netflix looked at the potential demographics for such a show. First by taking into account the popularity of the original UK show, and then observing that people who enjoyed the original also enjoyed watching programming featuring actor Kevin Spacey and directed by David Fincher, Netflix believed it had a winning combination.

The results speak for themselves. Big data should not only be used to tell what works and what does not, but also to plan for the future and recognise demand in places that might not be immediately obvious in order to deliver something fresh to customers.

Use data to create a personal connection

By using Big Data to influence choices in what shows and movies they commissioned, Netflix reaches audiences that were previously left ignored. With the data collected on viewing habits, Netflix can build user profiles that can efficiently recommend content that they know certain user groups will enjoy. For instance when was the last time you actually used the search feature to find content? In fact more than 80% of the shows people watch on Netflix are found through the recommendation engine rather than through other means.

The recommendation engine relies on an extensive amount of detail oriented tags to suggest similar content to viewers. For example if you watched the X-Files and The Goonies then at the top of your recommended content will sit Stranger Things, since it is a combination of sci-fi horror and children growing up in the 80s. Netflix use big data to tailor the customer experience to their specific preferences, personalisation goes a long way to ensuring brand loyalty.

“If the Starbucks secret is a smile when you get your latte… ours is that the Web site adapts to the individual’s taste.” – Reed Hastings, Chairman / CEO Netflix

Not all data is useful

Recently Netflix issued a statement that the company had made the decision to end user reviews. Reviews are one of the most prevalent ways that consumers use to make a decision whether to invest their time in a show or film. At first this practice might seem unusual, but by analysing the effects scores have on viewership, Netflix found that the negatives outweigh the positives. Audiences normally avoid poorly reviewed films instinctively, despite the fact that reviews are subjective.

Netflix understands that not all users are cinephiles who only want to watch award winning films, some just enjoy the lesser promoted movies. By removing user reviews, Netflix is using other methods to judge whether content is worth continuing to license such as the completion rate of a programme i.e how many users who started watching a programme actually finished it and what was the common cut off point.

A poor review can stem from a number of reasons with many people leaving poor reviews without even viewing due to political or social reasons. Meanwhile the time viewed provides an honest and clear method for scoring popularity. This information can then be combined with the aforementioned user profiling to target that content towards users who might have otherwise ignored such programming.

At the end of the day Netflix desires for its users to explore its full library and not just stick to an insular bubble of top rated content. By dismissing reviews, Netflix recognises that not all data can be useful and instead perhaps it is best to look into other metrics. The key takeaway here is that Big Data can be used to challenge industry standards and recognise that sometimes they can be harmful to your own model.

Use Big Data to spot what consumers really want

All of these points feed into how Netflix fundamentally changed the way people now consume television shows. Binge watching, was once limited by format, unless a series had been released on a VHS/DVD box set or a channel was airing a marathon, then you were unlikely to watch even two episodes of the same series concurrently. Netflix fostered an ecosystem where binge watching is actively encouraged. With its origins in providing mail order DVDs, Netflix understood the power of allowing audiences to watch content whenever they want, thus informing the revolutionary decision for a TV series to drop in its entirety rather than in weekly installments.

Netflix originals are tailored from the feedback to these changes as the episodic format is designed so that content is consumed rapidly, one episode after another. This is achieved by implementing skip intro buttons, a timer counting down to the next episode and shows which have a greater level of complexity in plotlines than a weekly soap since it can be assumed the viewer has already seen the previous episode.

Understanding that consumers were responding positively to these changes, Netflix began to pick up older shows that many viewers may have only previously caught the odd episode of and encouraged them to binge watch through the whole series. The creators of the critically acclaimed Breaking Bad attribute this to the show’s long running success, with viewers able to catch up on the series first few seasons in case they missed the initial broadcast on the AMC Network. All of this goes to show that Big Data can be used to foster positive consumer habits and disrupt traditionally held business models.

The finale

Ultimately Big Data is a tool that businesses can use to further understand the needs and desires of their customer base. In an age where personalisation and convenience are expected, Big Data can form the core to implementing better practices and spot key gaps in the market for disruption. The key to Netflix’s success is by not just collecting huge quantities of data, but analysing it to create better business practices, and then implementing them at all levels of the business.

If you are interested in further articles on Big Data, check out our other latest.